1. A system (200) for inspecting a shipping container, the system comprising:
a plurality of cameras (202) installed at a plurality of entry gates (204), wherein
the plurality of cameras (202) captures one or more images of the container passing
through the plurality of the entry gates;
an inspection device (206) operatively coupled to the plurality of cameras (202)
to receive captured images and process the one or more images using a machine
learning model;
wherein the machine learning model is configured to determine: a container
number recognition, a marking code sequence, presence of a seal on the container,
presence of a hazardous sign on the container, and presence of a damage on the
container.
2. The system (200) as claimed in claim 1, wherein:
the one or more images of the container includes images of different portions
of the container,
the different portions of the container includes images of container’s rear, front,
sides and/or top.
3. The system (200) as claimed in claim 1, wherein the container number recognition is
determined using convolution neural network (CNN) by matching the container number
recognition with a checksum and performing selection of highest occurring characters(with above threshold probability) in the same position in different frames.
4. The system (200) as claimed in claim 1, wherein the damage on the container includes
type of damage such as at least one of tear, hole, scratch, rust, and dent.
5. The system (200) as claimed in claim 1, wherein the determination of the damage
includes determination of the degree of the container damage from a first category, a
second category, a third category and a fourth category, wherein the first category indicates that the container is non-damaged, the second category indicates that the container is slightly damaged, the third category indicates that the container is seriously
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damaged, and the forth category indicates a category other than the first, second and
third categories.
6. The system (200) as claimed in claim 1, wherein the presence of the damage of the
container is determined by:
providing a supervised techniques and unsupervised techniques,
the supervised techniques includes labelling the one or more images of the
shipping container and determining the type of damage based on the labelling of the
one or more images,
the unsupervised techniques includes comparing one or more captured images
of the shipping container with one or more images of the shipping container without
damage.
7. The system (200) as claimed in claim 1, wherein each character of the marking code
sequence depicts length, height, and type of the shipping container.
8. The system (200) as claimed in claim 1, wherein the presence of hazardous sign is
determined by classifying the hazardous sign using convolution neural network (CNN).
9. The system (200) as claimed in claim 1, wherein the presence of the damage on the
container is determined by classifying the damage into high priority damage.
10. The system (200) as claimed in claim 9, wherein for high priority damage, the inspection device is configured to send an alert to an operator.